Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale
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Abstract
To learn how genomic sequence influences multiscale three-dimensional (3D) genome architecture, this manuscript presents a sequence-based deep-learning approach, Orca, that predicts directly from sequence the 3D genome architecture from kilobase to whole-chromosome scale. Orca captures the sequence dependencies of structures including chromatin compartments and topologically associating domains, as well as diverse types of interactions from CTCF-mediated to enhancer-promoter interactions and Polycomb-mediated interactions with cell-type specificity. Orca enables various applications including predicting structural variant effects on multiscale genome organization and it recapitulated effects of experimentally studied variants at varying sizes (300 bp to 90 Mb). Moreover, Orca enables in silico virtual screens to probe the sequence basis of 3D genome organization at different scales. At the submegabase scale, it predicted specific transcription factor motifs underlying cell-type-specific genome interactions. At the compartment scale, virtual screens of sequence activities suggest a model for the sequence basis of chromatin compartments with a prominent role of transcription start sites.
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Existing sequence-to-function methods involve a trade-off between input sequence length and prediction resolution, limiting their modality scope and performance.
“Existing methods involve a trade-off between input sequence length and prediction resolution, thereby limiting their modality scope and performance1–5.”
From Advancing regulatory variant effect prediction with AlphaGenome (2026) · cites this paper as [3]
The Introduction (page unavailable) says earlier sequence models did not predict large-scale genome organization beyond 1 Mb, limiting predictions of large structures and structural-variant effects. The Results describe using coarser resolution at longer distances and report a small improvement in local predictions from larger sequence context. The paper does not establish that a length–resolution trade-off in existing methods limits their modality scope or overall performance. (LLM, conf=0.83)
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- Authors
- Zhou, J.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Zhou, Jian
- ORCID
- Zhou, Jian
- Venue
- Nature Genetics
- Published
- 2022-05
- Volume
- 54
- Issue
- 5
- Pages
- 725-734
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC9186125
- OpenAlex
- W4280512834
- Cited by
- 177
- References
- 48
- Citations by year
- 2026: 45 · 2025: 36 · 2024: 49 · 2023: 31 · 2022: 15 · 2021: 1
- FWCI
- 11.00
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Cancer Prevention and Research Institute of Texas (RR190071)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (DP2GM146336)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
- Keywords
- 3D genome organization, sequence-based prediction, ORCA, chromatin compartments, topologically associating domains, enhancer-promoter interactions, cell type specificity, transcription factor binding motifs, transcription start sites, virtual screening, deep learning
- MeSH terms
- Chromatin, Chromosomes, Promoter Regions, Genetic, Transcription Factors, Genome, Polycomb-Group Proteins
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